Assessment of the Arctic surfclam (Mactromeris polynyma) stocks of Quebec coastal waters in 2020
Bibliographic record
Abstract
This research paper presents the Arctic surfclam biology and fishing activities. It also presents data and methodologies used to prepare the Quebec inshore waters Arctic surfclam stock assessment after the 2020 fishing season. This information was presented at the peer review meeting held virtually on February 22, 2021. Mean annual Arctic surfclam landings in Quebec totalled 587 t from 2018 to 2020, an 8% decrease compared with the 2015–2017 period. The North Shore accounted for 99% of landings and the Magdalen Islands for 1%. The annual Total Allowable Catch (TAC) for the 2018–2020 period averaged over 80% in areas 3A and 3B. There was no fishing in areas 1A and 5B in 2018 and Area 2 was fished in 2018 only. There was no fishing in Area 1B from 2018 to 2020 and areas 4C and 5A remain unexploited. Mean catches per unit effort (CPUE) from 2018 to 2020 are above the time series (1993–2019) median for Area 3A but below it for areas 1A, 2, 3B, 4A, 4B and 5B. Mean sizes at landing of surfclams from 2018 to 2020 are above the time series median for areas 2, 3A, 4B and 5B, but below it for areas 1A, 3B and 4A. The exploitation rate in each area (based on the dredged surface area) was below the recommended rate of 3% in all fishing areas. According to the existing decision rules, only Area 3A meets all the conditions for a 6% quota increase. Maintaining the current quota in the other areas should not affect the status of the resource. Fishing effort in one fishing area should be distributed within and among beds in order to limit the possibility of local overexploitation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".